MARC보기
LDR04288cmm u2200577 i 4500
001000000322034
003OCoLC
00520230613113651
006m d
007cr cnu|||unuuu
008191210t20202020maua ob 001 0 eng d
020 ▼a 0262358522 ▼q (electronic bk.)
020 ▼a 9780262358521 ▼q (electronic bk.)
020 ▼z 9780262044004
035 ▼a 2378911 ▼b (N$T)
035 ▼a (OCoLC)1130235839
037 ▼a 11805 ▼b MIT Press
037 ▼a 9780262358521 ▼b MIT Press
040 ▼a MITPR ▼b eng ▼e rda ▼e pn ▼c MITPR ▼d OCLCF ▼d N$T ▼d YDX ▼d EBLCP ▼d OCLCQ ▼d OCLCO ▼d ORE ▼d 248032
049 ▼a MAIN
050 4 ▼a HQ1190 ▼b .D549 2020eb
08204 ▼a 305.42 ▼2 23
1001 ▼a D'Ignazio, Catherine, ▼e author.
24510 ▼a Data feminism / ▼c Catherine D'Ignazio and Lauren F. Klein. ▼h [electronic resource]
264 1 ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c [2020]
264 4 ▼c 짤2020
300 ▼a 1 online resource (xii, 314 pages) : ▼b color illustrations
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
4901 ▼a <strong> ideas series
504 ▼a Includes bibliographical references (235-301) and indexes.
5050 ▼a Introduction: Why data science needs feminism -- Examine power : the power chapter -- Challenge power : collect, analyze, imagine, teach -- Elevate emotion and embodiment : on rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints -- Rethink binaries and hierarchies : "What gets counted counts" -- Embrace pluralism : unicorns, janitors, ninjas, wizards and rock stars -- Consider context : the numbers don't speak for themselves -- Make labor visible : show your work -- Conclusion: Now let's multiply.
520 ▼a A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom Data science for whom Data science with whose interests in mind The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics--one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves." Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
5880 ▼a Description based on online resource (viewed May 29, 2020).
590 ▼a Master record variable field(s) change: 050
650 0 ▼a Feminism.
650 0 ▼a Feminism and science.
650 0 ▼a Big data ▼x Social aspects.
650 0 ▼a Quantitative research ▼x Methodology ▼x Social aspects.
650 0 ▼a Power (Social sciences)
650 7 ▼a Big data ▼x Social aspects. ▼2 fast ▼0 (OCoLC)fst01983622
650 7 ▼a Feminism. ▼2 fast ▼0 (OCoLC)fst00922671
650 7 ▼a Feminism and science. ▼2 fast ▼0 (OCoLC)fst00922745
650 7 ▼a Power (Social sciences) ▼2 fast ▼0 (OCoLC)fst01074219
655 4 ▼a Electronic books.
7001 ▼a Klein, Lauren F., ▼e author.
830 0 ▼a <strong> ideas series.
85640 ▼3 EBSCOhost ▼u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2378911
938 ▼a EBSCOhost ▼b EBSC ▼n 2378911
990 ▼a 관리자
994 ▼a 92 ▼b N$T